Who Should Attend?
This program is most appropriate for individuals interested in learning about machine learning, with a focus on recent algorithms, like deep learning. You’ll learn the mathematics and statistics at the foundation of modern machine learning and get hands-on training in the latest machine learning software, using Google TensorFlow platform.
You should have a strong background in computing (e.g., Python, Matlab, SAS, etc.; any modern computing language), to be capable of learning how to use and apply modern machine learning software. For participants who also have a strong mathematical and statistical background (strength in calculus and in basic statistics, at the senior undergraduate level), the opportunity to understand the fundamentals of machine learning will be available. Strength in mathematics and statistics is a significant plus; however, it is not required to benefit from the hands-on software portion of the program.
More about this program
The broad areas of emphasis for the five-day program are as follows:
- Basic concepts in machine learning
- Introduction to model building
- Scaling to “big data” with stochastic gradient descent
- Backpropagation as an efficient computation method
- Deep convolutional neural networks
- Image analysis
- Image segmentation, object detection and object localization
- Methods for natural language processing
- Word embeddings
- Recurrent neural networks
- Temporal convolutional neural networks
- Data synthesis, with an emphasis on images
- Generative adversarial network (GAN)
- Deep networks for GAN
- Learning and applications of GAN
- Reinforcement Learning
- Basic concepts for optimal policies in complex environments
- Q-learning and leveraging deep networks
- Applications of reinforcement learnings
This five-day program will offer lectures on the mathematics and statistics at the heart of machine learning, plus hands-on training about implementing machine learning tools with the TensorFlow software platform. Each day, material will be discussed at three levels. First, concepts will be presented in an intuitive manner, with light emphasis on the mathematical details. The second portion of each day will then examine the underlying mathematics and statistics of the machine learning algorithms in greater detail. Finally, the third portion of each day will focus on software implementation in TensorFlow.
Each day will be arranged as follows:
Lecture 1: Mathematically-light introduction to the focus of the day
Lecture 2: Mathematically rigorous discussion of the focus of the day
Software discussion and hands-on training with TensorFlow
Breakout rooms, for assistance with the material of the day
At the end of the program, you should be able to use TensorFlow to implement the latest machine learning methods for analyzing images, video, and natural language (text). For those with a strong mathematical background, the underlying methodology of machine learning will also be covered. You will be given assignments to test your knowledge of the material, so you can get a sense of how well you have absorbed these concepts. Breakout sessions will also be held to offer clarification on concepts and help with hands-on software implementations on provided example datasets.